still in progess, added a ton of code for remote

from matplotlib.image import composite_images

from WorkingPyDemo import *

import paramiko

def setup_remote_sftpclient():

    client = paramiko.SSHClient()

    client.load_system_host_keys()

    client.connect("192.168.0.107", username="elphel")

    sftp_client = client.open_sftp()

    return sftp_client

def remove_noise(images, which_sensor):

    same_sensor_images = []

    which_sensor = str(which_sensor)

        # print(np.array(image_object)[1:] + average_image)

        average_image = np.array(image_object)[1:] + average_image

    return average_image/len(same_sensor_images)

scenes = file_extractor(folder_name)

images = image_extractor(scenes)

average_image = remove_noise(images,"7")

def remote_remove_noise(images, which_sensor):

    sftp_client = setup_remote_sftpclient()

    averages = []

    same_sensor_images = []

    which_sensor = str(which_sensor)

    first_image = sftp_client.open(images[0])

    average_image = np.array(Image.open(first_image))[1:]

    for i, image_name in enumerate(images):

        if int(which_sensor) > 9:

            if image_name[-7:-5] == which_sensor:

                same_sensor_images.append(image_name)

        else:

            if image_name[-7:-5] == "_" + which_sensor:

                same_sensor_images.append(image_name)

    images = []

    for i, image_name in enumerate(same_sensor_images):

        # print(image_name)

        image_object = sftp_client.open(image_name)

        image_object = Image.open(image_object)

        images.append(np.array(image_object)[1:])

        # print(np.array(image_object).shape)

        # print(np.array(image_object)[1:] + average_image)

        if (i % 100 == 0) and i!=0:

            image_object = np.mean(np.array(images),axis = 0)

            # print(image_object.shape)

            averages.append(image_object)

            # print(average_image.shape)

            images = []

    image_object = np.mean(np.array(images))

    averages.append(image_object)

    sftp_client.close()

    return np.mean(averages,axis=0)

def remote_file_extractor(headname = "/media/elphel/NVME/lwir16-proc/te0607/scenes/"):

    """Find all the files in the directory

    Parameters:

        dirname (str): the directory name

    Returns:

        files (list): a list of all the files in the directory

    """

    client = paramiko.SSHClient()

    client.load_system_host_keys()

    client.connect("192.168.0.107", username="elphel")

    sftp_client = client.open_sftp()

    # sftp_client.listdir("media/elphel/NVME/lwir16-proc/te0607/scenes/")

    dirs_in_scenes = sftp_client.listdir("/media/elphel/NVME/lwir16-proc/te0607/scenes/")

    scenes = []

    for i, curr_folder in enumerate(dirs_in_scenes):

        if "." not in curr_folder:

            smaller_dirs = sftp_client.listdir(headname + curr_folder)

            for small_folder in smaller_dirs:

                scenes.append(headname + curr_folder + "/" + small_folder)

    return scenes

def remote_image_extractor(scenes):

    sftp_client = setup_remote_sftpclient()

    image_folder = []

    for scene in scenes:

        files = sftp_client.listdir(scene)

        for file in files:

            if file[-5:] != ".tiff" or file[-7:] == "_6.tiff":

                continue

            else:

                image_folder.append(os.path.join(scene, file))

    sftp_client.close()

    return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor

def remove_the_noise(new_image, average_image):

    original_image_min = np.min(newimage)

    original_image_max = np.max(new_image)

    adjusted_image = new_image - average_image

    adjusted_image = adjusted_image - np.min(adjusted_image)

    adjusted_image = adjusted_image*original_image_max/np.max(adjusted_image)

    adjusted_image = adjusted_image + original_image_min

    return adjusted_image

def color_adjust(visual_array):

    min_of_errors = np.min(visual_array)

    adjusted_array = visual_array - min_of_errors

    adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))

    adjusted_array = adjusted_array/np.max(adjusted_array)

    # print(adjusted_array)

    # print(np.max(adjusted_array))

    return adjusted_array

print(np.max(average_image))

print(np.min(average_image))

plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=255)

if __name__ == "__main__":

    scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")

    # images = remote_image_extractor(np.random.choice(scenes,10000,replace = False))

    images = remote_image_extractor(scenes)

    # average_image = remote_remove_noise(images,"10")

    average_image = np.array(Image.open("hopefullyaverage.tiff"))

    # average_savable_image = Image.fromarray(average_image)

    # average_savable_image.save("hopefullyaverage.tiff")

    # print(np.max(average_image))

    # print(np.min(average_image))

    # average_image = color_adjust(average_image)

    plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)

    plt.show()

    # print(len(images))

    sftp_client = setup_remote_sftpclient()

    print(len(images))

    for i, item in enumerate(images[22000:22030]):

        if item[-7:-5] == "10":

            print(i)

            print(item)

    print(images[22016])

    test_image = sftp_client.open(images[22016])

    test_image = Image.open(test_image)

    test_image = np.array(test_image)[1:]

    newimage = Image.fromarray(test_image - average_image)

    newimage.save("NoInterference.tiff")

    plt.subplot(121)

    plt.imshow(color_adjust(test_image),cmap='gray',vmin = 0, vmax=1)

    plt.subplot(122)

    plt.imshow(color_adjust(test_image-average_image),cmap='gray',vmin = 0, vmax=1)

    plt.show()

    # print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]])))

print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))

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    # print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))

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